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RETIREMENT PROSPECTS FOR THE MILLENNIALS: WHAT IS THE EARLY PROGNOSIS?
Richard W. Johnson, Karen E. Smith, Damir Cosic, and Claire Xiaozhi Wang
CRR WP 2017-17 November 2017
Center for Retirement Research at Boston College Hovey House
140 Commonwealth Avenue Chestnut Hill, MA 02467
Tel: 617-552-1762 Fax: 617-552-0191 http://crr.bc.edu
Richard W. Johnson and Karen E. Smith are senior fellows at the Urban Institute. Damir Cosic is a research associate and Claire Xiaozhi Wang is a research assistant at the Urban Institute. The research reported herein was pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Retirement Research Consortium. The findings and conclusions expressed are solely those of the authors and do not represent the views of SSA, any agency of the federal government, the Urban Institute, or Boston College. © 2017, Richard W. Johnson, Karen E. Smith, Damir Cosic, and Claire Xiaozhi Wang. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.
About the Center for Retirement Research
The Center for Retirement Research at Boston College, part of a consortium that includes parallel centers at the University of Michigan and the National Bureau of Economic Research, was established in 1998 through a grant from the Social Security Administration. The Center’s mission is to produce first-class research and forge a strong link between the academic community and decision-makers in the public and private sectors around an issue of critical importance to the nation’s future. To achieve this mission, the Center sponsors a wide variety of research projects, transmits new findings to a broad audience, trains new scholars, and broadens access to valuable data sources.
Center for Retirement Research at Boston College Hovey House
140 Commonwealth Ave Chestnut Hill, MA 02467
Tel: 617-552-1762 Fax: 617-552-0191 http://crr.bc.edu
Affiliated Institutions: The Brookings Institution
Syracuse University Urban Institute
Abstract
Various policy developments and long-term economic, social, and demographic trends
raise worrisome questions about the financial security of future retirees. An erosion in
employer-sponsored defined benefit pension coverage and the increase in Social Security’s full
retirement age could shrink future benefits. Stagnating employment and earnings for men could
threaten future retirement security, because retirement benefits and the capacity to save depend
on lifetime earnings. The financial crisis, Great Recession, and collapse of the housing market in
the second half of the previous decade could significantly disrupt retirement savings. This paper
assesses retirement prospects for future generations, with a special focus on the late Generation-
X and the Millennial generations. Because retirement outcomes depend on how much people
earned and saved when they were younger, the analysis compares trends in employment,
earnings, pension coverage, and wealth during working ages across cohorts, using data from
the Current Population Survey and the Survey of Consumer Finances. The analysis also projects
age-70 incomes for future generations using DYNASIM4, the Urban Institute’s dynamic
microsimulation model.
This paper found that:
• Many recent trends threaten future retirement security, including continuing declines in
men’s employment before age 55, stagnating median earnings for men, and a sharp
decline in median household wealth after 2007.
• Other trends are more encouraging. Gen-X and Millennial women worked and earned
more in their 20s and 30s than now-retired women did at those ages, and Millennial men
and women are much more likely to have a four-year college degree than previous
cohorts.
• Projections show that median age-70 income will be higher for Gen-Xers and Millennials
than previous generations as earnings grow over time.
• However, a larger share of retired Gen-Xers and Millennials will be unable to replace at
least three-quarters of their pre-retirement earnings, according to the projections, and will
see their living standards decline when they retire.
The policy implications of this paper are:
• How employment, earnings, and savings patterns evolve over the next three decades will
shape Millennials’ retirement incomes.
• Policy choices regarding retirement programs, especially strategies to address Social
Security’s long-term financing gap, will significantly affect the retirement security of
future generations.
Introduction
Changes in retirement programs and ongoing economic, social, and health-care-related
trends raise worrisome questions about the financial security of future retirees. The increase in
Social Security’s full retirement age will reduce benefits for workers who retire in the future, and
the system’s long-term financing problems could lead to additional benefit cuts in two decades
unless Congress addresses the funding shortfall. Private-sector employers have moved away
from defined benefit (DB) pensions to defined contribution (DC) retirement plans over the past
three decades, shifting much of the responsibility for retirement saving from employers to
employees and reducing future retirement benefits for many workers (Morrissey, 2016; Munnell,
2014). Falling labor supply among middle-aged men (Council of Economic Advisers, 2016) and
stagnant earnings for lower- and moderate-income men (Mishel, 2015) threaten future retirement
security, because Social Security benefits and the capacity to save for retirement depend on
lifetime earnings. Future retirees will need more money than earlier generations as health care
costs and indebtedness rise (Hatfield et al., 2016; Karamcheva, 2016), and retirement savings
must last longer as retirees’ life expectancy grows.
The financial crisis, Great Recession, and collapse of the housing market in the second
half of the previous decade led to unusually high and long-lasting unemployment and wiped out
trillions of dollars of household wealth (Grusky, Western, and Wimer, 2011; Smeeding, 2012;
Wolff, 2016). Lost earnings and wealth can derail retirement savings. The recession hit younger
workers especially hard. They were more likely to lose their jobs than older workers (Farber,
2015), and the economic consequences of a layoff can persist for decades, leading to lower
earnings on future jobs (Davis and Van Wachter, 2011). Moreover, people who graduate from
college during a recession often have trouble finding a good job, suppressing their earnings for
years (Oreopoulos, von Wachter, and Heisz, 2012). Consequently, the Great Recession could
significantly disrupt retirement savings for people born in the late 1970s and early 1980s, who
were in their 20s during the early part of this decade. On the other hand, relatively few younger
people own a home or hold much wealth, so the collapse in housing and equity prices in the
wake of the financial crisis probably did not affect them as much as it affected older people.
Other economic and demographic trends, however, are more encouraging. Women who
retire in coming decades will have worked in paid employment more and earned more than
previous generations (Goldin and Mitchell, 2017), enabling then to accumulate more Social
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Security benefits and retirement savings in their own names. Increases in the national average
wage raises Social Security payments for all beneficiaries, even for those with relatively low
earnings. Widows are especially likely to be impoverished (Sevak, Weir, and Willis,
2003/2004), but the shrinking gender gap in life expectancy (Trovato and Heyen, 2006) will
reduce future widowhood rates. In addition, people are working longer than previous
generations (Johnson and Wang, 2017), raising their lifetime earnings, future Social Security
benefits, and capacity to save for retirement.
Given these conflicting trends, it is perhaps not surprising that there is no consensus
about how future generations will likely fare in retirement. Several studies warn of a looming
retirement crisis, predicting that in coming decades many older adults will live in or near poverty
and a majority will be unable to maintain their preretirement living standards (Munnell, Hou, and
Webb, 2014; Rhee, 2013). Other studies are more sanguine, concluding that most people are
saving adequately and that economic growth will boost future retirement incomes (Biggs and
Schieber, 2014; Butrica, Smith, and Iams, 2012; Scholz, Seshadri, and Khitatrakun, 2006).
This report used household survey data from the past five decades and a dynamic
microsimulation model to assess retirement prospects for future generations, with a special focus
on the late Gen-X and Millennial generations. Because retirement outcomes depend on how
much people earned and saved when they were younger, much of our analysis compared trends
in employment, earnings, pension coverage, and wealth during working ages across cohorts. We
projected future incomes at age 70, accounting for working-age outcomes that have already
occurred. The analysis focused on outcomes for adults born between 1976 and 1980, who we
labeled late Gen-Xers, and adults born between 1981 and 1990, the early Millennials. We
excluded from our analysis Millennials born after 1990, because their labor market experience is
too thin to draw firm conclusions about their long-term earnings potential and capacity to save
for retirement. We compared outcomes for these cohorts to those born earlier, including pre-
boomers (born before 1946), early boomers (born between 1946 and 1955), and late boomers
(born between 1956 and 1965).
Our results suggest retirement security for late Gen-Xers and Millennials will be shaped
by many of the same forces that are already beginning to buffet the financial security of current
retirees, including the erosion of DB pension plans and rising debt levels. So far, outcomes for
Millennials are not dramatically worse than those for previous recent cohorts, although the
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steady generational improvement in economic status that defined American society in the middle
of the twentieth century appears to have ended, at least for now. Men’s labor force participation
rates continue to decline before age 55 and their median wage remains stagnant. Gen-X and
Millennial women are earning more than the Boomers did, but Millennials are not earning more
than Gen-Xers. People born after 1970 are not accumulating household wealth any faster than
those born in the 1960s, reversing the generational growth experienced by earlier cohorts, and
Millennials are less likely to own a home than earlier generations. However, the collapse in
home prices and the stock market in the late 2000s complicate these generational comparisons.
The most encouraging development for Millennials is the growth in college graduation rates,
which raises their future earnings potential.
Our projections show that median age-70 income will be higher for Gen-Xers and
Millennials than previous generations, but a greater share may experience falling living standards
when they stop working. Using a measure of retirement income that includes payouts that could
be collected from an actuarially fair annuity valued at 80 percent of a retiree’s financial assets
and retirement accounts, we found that 40 percent of 70-year-olds born between 1976 and 1985
would be unable to replace at least 75 percent of the inflation-adjusted average annual earnings
they and their spouse received from ages 50 to 54, under the assumption that average wages
grow at the same rate as they did between 1966 and 2015. By comparison, replacement rates at
age 70 would likely fall short of the 75 percent threshold for 32 percent of those born between
1936 and 1945 and for 30 percent of those born between 1956 and 1965.
Data and Methods
To assess retirement prospects for people born in the 1980s and late 1970s, we compared
employment, earnings, pension coverage, and household wealth at younger ages for several
generations, using household survey data. We also compared projections of retirement incomes
for different birth cohorts, generated by our dynamic microsimulation model. Our tabulations
report all financial amounts in constant 2015 dollars, adjusted by the change in the consumer
price index.
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Measuring Recent Economic Trends before Retirement
We used household survey data spanning several decades from the Current Population
Survey’s (CPS) Annual Social and Economic (ASEC) supplement and the Survey of Consumer
Finances (SCF) to examine long-term trends in demographic and economic outcomes. Although
the surveys do not follow the same sample of households over time, we created synthetic cohorts
by combining information from interviews completed in various years by respondents born in the
same period. We then compared aggregate outcomes across cohorts at various ages. When
comparing outcomes across cohorts, however, we must recognize the sometimes-substantial
differences in macroeconomic conditions, such as the unemployment rate and typical investment
returns, that confronted each generation at particular ages. For example, high unemployment
rates and slow wage growth during and immediately after the Great Recession and the collapse
in housing and equity values in 2007 and 2008 complicate cohort analysis. All estimates were
computed at the individual level, not the household level.
The CPS is a monthly survey of about 60,000 households conducted by the U.S. Census
Bureau for the Bureau of Labor Statistics (BLS) that collects demographic and employment data.
Every March the ASEC collects additional information from CPS respondents on income
received during the previous year, employer-sponsored retirement plans during the previous
year, and homeownership. With CPS/ASEC data from 1966, 1971, 1976, 1981, 1986, 1991,
1996, 2001, 2006, 2011, and 2016, we created synthetic five-year cohorts for the birth years
1931 to 1935 through 1986 to 1990. Members of our youngest cohort were ages 26 to 30 in
2016, and members of our oldest cohort were ages 31 to 35 in 1966. We accessed CPS data
through the Integrated Public Use Microdata Series (Ruggles et al., 2015).
We used CPS/ASEC data to examine trends in educational attainment, labor force
participation, full-time employment, marriage rates, homeownership rates, and, for full-time
workers, median earnings and participation rates in employer-sponsored retirement plans. Full-
time employment, earnings, and pension coverage data in the CPS/ASEC refer to outcomes in
the previous year, so our cohorts are one year younger for those comparisons. We defined full-
time employment as working at least 35 hours per week. Data on homeownership were not
available before 1976 and data on employment-based retirement plans were not available before
1980. When computing homeownership rates, we counted only household heads and their
spouses as homeowners if the owner occupied the home; other people living in an owner-
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occupied home were classified as non-homeowners. The analysis generally examined outcomes
separately for men and women. Results are reported graphically in the body of the report, but
the figures exclude certain cohorts to improve readability. Appendix tables report results for all
cohorts.
We used data from the Survey of Consumer Finances (SCF) to examine trends in
household wealth levels. The SCF is a national, cross-sectional survey of U.S. families that
began in 1983. Sponsored by the Federal Reserve Board and conducted by NORC at the
University of Chicago since 1992, the SCF is widely regarded as the premier data source on
household wealth (Czajka, Jacobson, and Cody, 2003). Every three years, it interviewed
between 4,500 and 6,500 families covering all economic strata. Sampling began with a
geographically based random sample, which was then supplemented with a sample of
disproportionally wealthy families to reflect ownership of certain assets. Our sample included
only household heads and their spouses, if married. For married people, we divided reported
household wealth by two. Following the approach we used with the CPS/ASEC, we grouped
respondents into six-year birth cohorts—from the 1928 to 1933 cohort through the 1976 to 1981
cohort—and measured their wealth every six years, in 1989, 1995, 2001, 2007, and 2013, the
most recent year available when we completed our analysis. This approach allowed us to
compare household wealth at the same age for people born in different years. Members of our
youngest SCF cohort were ages 32 to 37 in 2013, and members of our oldest cohort were ages 56
to 61 in 1989. Because our SCF analysis included only household heads and their spouses,
young adults who were still living with their parents were excluded. Because people who
recently entered adulthood are leaving home and starting their own households later than
previous generations (Furlong, 2016; Lee and Painter, 2013), our SCF sample of younger adults
might include a disproportionate share of relatively successful people who have already started
their own households, biasing our wealth estimates upwards.
The financial measures we examined were total net worth, retirement account balances,
financial wealth (including retirement account balances), housing wealth, and debt. Retirement
account balances included the value of individual retirement accounts (IRAs), Keogh accounts,
and employer-sponsored retirement accounts, such as 401(k) plans. Financial wealth consisted
of retirement account balances plus financial assets held outside of retirement accounts,
including the value of bank accounts, certificates of deposit, annuities, trusts, stocks, bonds,
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mutual funds, and the cash value of life insurance. We measured housing wealth as the gross
value of a primary home. Debt included housing debt, outstanding installment loans, outstanding
credit card balances, and any other debt held by a household. Total net worth equaled the sum of
financial wealth, housing wealth, and other nonfinancial wealth (which included the value of
vehicles, business interests, real estate except for a primary home, and other real assets), minus
debt.
Projecting Retirement Outcomes
To project future retirement income, we used DYNASIM4, a dynamic microsimulation
model designed to analyze the long-run distributional consequences of retirement and aging
issues. The model starts with a representative sample of individuals and families from the 2004
and 2008 Survey of Income and Program Participation (SIPP) and ages them year by year,
simulating key demographic, economic, and health events. For example, DYNASIM4 projects
that, each year, some people in the sample get married, have a child, or find a job. The model
projects that other people become divorced or widowed, stop working, begin collecting Social
Security, become disabled, or die. These transitions are based on probabilities generated by
carefully calibrated equations estimated from nationally representative household survey data.
The equations account for important differences by sex, education, earnings, and other
characteristics in the likelihood of various experiences. Other equations in DYNASIM4 project
annual earnings, savings, and home values. The model uses program rules—combined with
projections of lifetime earnings, disability status, and household income and wealth—to project
Social Security retirement and disability benefits and Medicaid coverage. For consistency with
Social Security’s projections about system finances, we generally use the same assumptions as
the Social Security and Medicare trustees. The appendix describes in more detail how
DYNASIM projects economic outcomes. For more information about DYNASIM, see Urban
Institute (2015) and Favreault, Smith, and Johnson (2015).
Using DYNASIM4, we projected outcomes for five 10-year birth cohorts: 1936 to 1945
(pre-boomers), 1946 to 1955 (early boomers), 1956 to 1965 (late boomers), 1966 to 1975 (Gen-
X), and 1976 to 1985 (late Gen-X, early Millennials). The analysis compared inflation-adjusted
income levels and retirement replacement rates at age 70. We focused on incomes at age 70
because most people have stopped working by then, but 70-year-olds are not so old that
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socioeconomic differentials in mortality rates could substantially bias our estimates.
Replacement rates were computed by dividing age-70 income by average annual earnings
received from ages 50 to 54, both expressed in inflation-adjusted dollars. We divided family
income by two for married adults to create a per capita measure.
We compared two measures of median annual per capita family income at age 70. The
traditional income measure counted cash income (earnings, Social Security, DB pensions,
Supplemental Security Income, interest, dividends, and rent), plus money withdrawn from
retirement accounts (based on historic withdrawal trends and required minimum distributions).
The alternative total potential income measure accounted for the shift away from DB pensions to
DC retirement accounts by adding to cash income the income stream that retirees would receive
if they annuitized 80 percent of their retirement accounts and other financial assets under
actuarially fair terms. Because relatively few retirees annuitize their assets and many do not
spend much of their wealth (Lockwood, 2012; Smith, Soto, and Penner, 2009), we examined
both income measures. Each measure used Social Security benefits scheduled under current law,
even though Social Security projects that it will be able to finance those benefits under existing
revenue forecasts only until 2034 (Board of Trustees, Federal Old-Age and Survivors Insurance
and Federal Disability Insurance Trust Funds, 2017), before late Gen-Xers and Millennials reach
age 70.
Future retirement income for late Gen-Xers and Millennials depends on how fast average
wages grow over the next three or four decades. The Social Security trustees assume that real
wages grow 1.2 percent per year in the long term (Board of Trustees, Federal Old-Age and
Survivors Insurance and Federal Disability Insurance Trust Funds, 2017), much faster than actual
growth over the past half century. Real annual wage growth averaged 0.81 percent between
1966 and 2015 and only 0.70 percent between 1973 and 2015, which excluded the high-growth
period from 1966 to 1973. Because of the uncertainty surrounding future wage growth, we
projected retirement income under three scenarios—a high-wage-growth scenario that assumed
real wages grow 1.2 percent per year, a moderate-wage-growth scenario that assumed real wages
grow 0.81 percent per year, and a low-wage-growth scenario that assumed real wages grow 0.70
percent per year.
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Results
We first report cohort comparisons for preretirement outcomes, based on historical data
from the ASEC/CPS and SCF. We then report retirement income projections from DYNASIM4.
Education
Men’s educational attainment surged with the early Baby Boomers and then tapered off
for men born in the 1950s and 1960s. Among men ages 26 to 30, 28 percent of those born
between 1946 and 1950 had a four-college degree, compared with 20 percent of those born five
years earlier, 17 percent for those born 10 years earlier, and 24 percent for those born 10 years
later (Figure 1). The Vietnam War draft, which men could avoid with an educational deferment,
appears responsible for the surge in college attendance for men in the 1946 to 1950 birth cohort,
who were 20 years old in the late 1960s (Card and Lemieux, 2001). For men born between 1951
and 1980, the share with a four-year college degree fluctuated between 24 percent and 28
percent. However, the share edged up to 29 percent for the early Millennials, born between 1981
and 1985, and surged to 34 percent for those born between 1986 and 1990, who were in their
early 20s during and immediately after the Great Recession and likely pursued higher education
because employment prospects were bleak (Mordechay, 2017). It remains to be seen whether the
recent educational surge for men will persist now that the job market has improved.
Educational attainment for women has improved steadily over the past 50 years (Figure
2). The share of women ages 26 to 30 with a four-year college degree increased from 11 percent
for those born between 1936 and 1940 to 20 percent for those born between 1946 and 1950—the
early Baby Boomers—to 33 percent for those born between 1976 and 1980—the late Gen-Xers.
This trend continued, and even accelerated, among Millennials, with 40 percent of women born
between 1986 and 1990 having completed college by ages 26 to 30. Since the 1961 to 1965 birth
cohort, women have been more likely than men to hold a four-year college degree. Millennials’
high level of educational attainment bodes well for their future earnings and retirement security.
Labor Force Participation
Millennial men were less likely to participate in the labor force in their 20s and early 30s
than previous cohorts (Figure 3). At ages 26 to 30, 88 percent of men born between 1986 and
1990 participated in the labor force, compared with 90 percent in the 1976 to 1980 birth cohort,
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93 percent in the 1961 to 1965 cohort, and 96 percent in the 1941 to 1945 cohort. Participation
rates at ages 21 to 25 were especially low for Millennial men born between 1986 and 1990, with
only 77 percent participating in the labor force. The low participation rates for Millennials may
simply reflect the high unemployment rates that existed early in their careers and discouraged
them from looking for work, or they may have been related to their high college attendance rates
(which may have also resulted from the poor job market). In fact, there is already some
indication that Millennial men’s participation rates may be catching up to those of earlier recent
cohorts; at ages 31 to 35, men born between 1981 and 1985 were just about as likely to
participate in the labor force as those born five years earlier. However, men born in the late
1970s were less likely to participate in the labor force than earlier cohorts, so the fact that
Millennial men are catching up to them may not be particularly encouraging.
A more worrisome trend for future retirement security is the long-term decline in labor
supply among men in their 40s and 50s. At ages 41 to 45, for example, male labor force
participation rates fell from 93 percent for the 1941 to 1945 birth cohort to 90 percent for the
1971 to 1975 birth cohort. This decline has been concentrated among men with no more than a
high school education, perhaps because technological change and increased globalization
reduced employers’ demand for low-skilled and middle-skilled workers (Aaronson et al., 2014;
Beaudry, Green, and Sand, 2016; Council of Economic Advisers, 2016; Juhn et al., 1991; Juhn
and Potter, 2006). Rising receipt of Social Security Disability Insurance benefits and the
growing opioid epidemic may also play a role in the growth of male labor force dropouts (Autor
et al., 2016; Autor and Duggan, 2003; Bound and Burkhauser, 1999; French and Song, 2014;
Krueger, 2017).
An encouraging sign for retirement security is the recent increase in labor force
participation among older men. Men born between 1951 and 1955 were 11 percentage points
more likely to participate in the labor force at ages 61 to 65 than those born 20 years earlier.
This recent surge reflects higher educational levels among older adults, changes in Social
Security rules that increased work incentives, and erosion in DB pension and retiree health
insurance coverage from private-sector employers (Friedberg and Webb, 2005; Gustman and
Steinmeier, 2015; Johnson, Davidoff, and Perese, 2003; Mermin, Johnson, and Murphy, 2007;
Song and Manchester, 2007).
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Working longer can significantly improve the lives of older adults. Extending the work
life and delaying retirement benefit take-up can bolster financial security at older ages (Maestas
and Zissimopoulos, 2010), because adults who work longer can receive higher monthly Social
Security benefits, accumulate more employer-sponsored pensions, save part of their additional
earnings, and shrink the period over which their retirement savings are spread. Butrica, Smith,
and Steuerle (2006) show that average annual retirement incomes rise 9 percent when older
adults work an additional year and 56 percent when they work an additional five years. Working
longer might also improve health and happiness at older ages by keeping people physically and
mentally active, allowing them to maintain social networks, and giving purpose to their lives
(Calvo, 2006). However, employment gains after age 65 have been concentrated among college
graduates (Johnson and Wang, 2017).
Millennial women’s labor force participation rates have not, to date, fallen as far behind
the participation rates of previous cohorts as they have for their male counterparts (Figure 4). At
ages 26 to 30, female participation rates were 74 percent for the 1981 to 1985 cohort and 75
percent for the 1986 to 1990 cohort, compared with 76 percent for the 1976 to 1980 cohort.
Although the Great Recession appears to have reduced Millennial women’s labor force
participation rates, the downward pressure created by the weak economy was somewhat offset by
the long-term generational increase in women’s labor supply. Among women ages 31 to 35, for
example, those in the 1981 to 1985 cohort were more likely to participate in the labor force than
women born before 1956. As other studies have noted, however, generational gains in women’s
labor force participation have slowed over the past two decades (Blau and Kahn, 2007). Across
generations, female labor supply tends to dip when women move through their 30s, as they work
less when raising children, but there is no evidence that women have become more likely over
the past two decades to leave the labor force to raise children (Goldin and Mitchell, 2017).
Women, like men, are also lengthening their careers, working more at older ages than earlier
generations (Goldin and Katz, 2016).
Full-Time Employment
The percentage of young men working full time has fallen sharply over the past decade
(Figure 5). At ages 26 to 30, 80 percent of men in the 1986 to 1990 birth cohort were employed
full time, down 6 percentage points relative to those born 10 years earlier and down 8 percentage
11
points relative to those born 25 years earlier. Full-time employment has also been slowly
declining at middle age, following the pattern we observed for labor force participation. At ages
46 to 50, men born between 1971 and 1975 were 7 percentage points less likely to work full time
than men born between 1946 and 1950. However, full-time employment rates at older ages
(after age 60) increased over the past two decades.
Full-time employment rates for women in their early 30s increased steadily for 30 years,
from those born in the first half of the 1930s through those born in the first half of the 1960s
(Figure 6). Since then, full-time employment rates have generally stabilized at about 60 percent
(although rates dipped for most ages in 2011, when unemployment rates were high). So far, full-
time employment rates for Millennial women is similar to full-time employment rates for recent
earlier cohorts and higher than the rates for cohorts born before 1955. As with older men, full-
time employment for older women has increased over the past 20 years.
Earnings
Although Millennial men have generally earned less than men born 30 or more years
earlier, their earnings so far have been roughly comparable to those in recent cohorts (Figure 7).
Among men ages 25 to 29 employed full-time, median annual earnings were $38,500 (in
inflation-adjusted 2015 dollars) for the 1986 to 1990 birth cohort, about 5 percent higher than the
median for the 1976 to 1980 cohort and the 1966 to 1970 cohort and 7 percent lower than the
median for the 1971 to 1975 cohort. However, median inflation-adjusted annual earnings at ages
25 to 29 were 29 percent higher for full-time male workers born between 1941 and 1945 than
those born between 1986 and 1990.
The recent stagnation in median earnings reflects deteriorating labor market prospects for
low- and middle-skilled men. Technological advancements, increasing globalization, and
declining union membership have increased earnings for men in the top of the quarter of the
earnings distribution, as earnings in the bottom half of the distribution fell or remained flat
(Gottschalk and Danziger, 2005; Holzer and Hlavac, 2012; Kopczuk, Saez, and Song, 2007;
Mishel, 2015; Rose, 2016). Rising health care costs have also increased the share of
compensation going to fringe benefits, suppressing growth in cash earnings (Burtless and
Milusheva, 2012).
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Millennial women, by contrast, are generally earning more than previous cohorts of
women employed full time (Figure 8). Median inflation-adjusted annual earnings for full-time
workers ages 25 to 29 was higher for the 1986 to 1990 cohort than for any other cohort going
back to 1936. Similarly, median inflation-adjusted annual earnings for full-time workers ages 30
to 34 was higher for the 1981 to 1985 cohort than any other cohort we observed except for the
1976 to 1980 cohort, which was only $100 higher. The evidence indicates that the long-term
growth in women’s earnings has continued through the Millennial generation, although it has
slowed substantially.
Employer-Sponsored Retirement Plan Coverage
The share of full-time workers in the CPS/ASEC reporting employer-sponsored
retirement plan coverage fell sharply across all age groups in 2016 relative to previous years.
Between 2011 and 2016, retirement plan coverage rates for the 1976 to 1980 cohort declined 5
percentage points for men employed full time (Appendix Table 7) and 8 percentage points for
women employed full time (Appendix Table 8). However, these declines seem to indicate a
problem with the recent CPS/ASEC retirement plan data. The survey questionnaire related to
pensions was redesigned in 2014, and comparisons of coverage rates using the old survey
instrument and the new survey instrument show that rates were much lower after the redesign
(Copeland, 2016). Moreover, coverage rates reported by the BLS’s National Compensation
Survey did not decline after 2014.
Because of the apparent problems with the survey redesign, we restricted the sample to
data from 1981 to 2011 and reran the tabulations. Participation rates in employer-sponsored
retirement plans among full-time male employees generally fell with each successive cohort
(Figure 9). For the 1951 to 1955 cohort, coverage rates peaked at ages 45 to 49 at 62 percent.
Among those born 10 years later, only 53 percent had coverage at that age. The decline in
unionization and the growth in service industries, where retirement plan coverage is less
common than in goods-producing industries, appear at least partly responsible for falling
coverage rates (Costo, 2006; Wiatrowski, 2004). However, coverage rates seem to be dropping
more slowly for people born between 1976 and 1990 than for previous cohorts. At ages 25 to 29,
full-time male employees born between 1981 and 1985 have about the same coverage rate as
those born between 1976 and 1980.
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Retirement plan coverage has also dipped for women working full-time, but the declines
have not been nearly as dramatic (Figure 10). For older cohorts, coverage among full-time
employees did not differ much by sex. At ages 45 to 49, female coverage rates peaked at 60
percent for the 1951 to 1955 cohort and fell to 54 percent for those born 10 years later. For more
recent cohorts, women working full-time had higher retirement plan coverage rates than their
male counterparts. At ages 25 to 29, women in the 1976 to 1980 cohort and the 1981 to 1985
cohort who worked full time were about as likely to participate in an employer-sponsored
retirement plan as their late Boomer counterparts born between 1961 and 1965.
Marriage
Marriage is an important source of retirement security, because it allows spouses to pool
resources and insure against risks and qualifies retirees for spouse and survivor benefits from
Social Security (and from employer pensions if they have them). However, the institution of
marriage has been eroding for decades for both men and women (Cherlin, 2010). For men, each
successive cohort has increasingly postponed marriage, and marriage rates for earlier cohorts
have plateaued in middle age at successively lower levels (Figure 11). At ages 51 to 55, 66
percent of men in the 1961 to 1965 cohort were married, compared with 77 percent of men born
20 years earlier. An encouraging sign is that marriage rates are no longer falling for men in their
late 30s. At ages 36 to 40, men in the 1976 to 1980 birth cohort are just about as likely to be
married as those born 5 or 10 years earlier. Marriage rates for men born between 1976 and 1990
have been increasing rapidly as they move through their 20s and early 30s, suggesting that men
are still delaying marriage but that the likelihood that they will ever marry is no longer dropping.
Marriage patterns are similar for women, who have increasingly delayed marriage over
the past four decades, perhaps to pursue education or a career (Figure 12). At ages 26 to 30, for
example, 81 percent of women born between 1941 and 1945 were married, compared with only
42 percent of women born between 1986 and 1990. As with men, however, the share of women
who were married in middle age no longer appears to be declining. Women in the 1976 to 1980
birth cohort were just as likely to be married at ages 36 to 40 as those in the 1971 to 1975 birth
cohort.
14
Homeownership
Homeownership is an important financial resource in retirement. Retirees can avoid
rental payments by owning a home, and homeowners may tap into their housing wealth to
supplement their retirement income. The overall homeownership rate—the number of owner-
occupied housing units divided by the total number of occupied housing units—increased from
World War II through 2005, when it hit a high of 69 percent, and then fell to 64 percent in 2017
following the financial crisis, Great Recession, and collapse of the housing bubble (Garriga,
Gavin, and Schlagenhuaf, 2006; U.S. Census Bureau, 2017). Much of the rise in homeownership
through 2005 resulted from economic growth that made homeownership more affordable and
from financial market innovations and regulatory changes that increased access to mortgage
finance (Garriga, Gavin, and Schlagenhuaf, 2006; Li, 2005). The aging of the large Baby Boom
generation, which moved into their 30s and 40s in the 1980s and 1990s, also appears to have
raised the homeownership rate, because homeownership generally becomes more common with
age, up to typical retirement ages.
However, our calculations of CPS/ASEC data that hold age constant show that
homeownership has been declining across the age span for several decades (Figure 13). Unlike
the Census Bureau’s homeownership rate, which shows the share of households in an owner-
occupied dwelling, our calculations show the share of adults who own a home. At ages 61 to 65,
the share of adults owning a home fell from 80 percent for the 1941 to 1945 birth cohort to 74
percent for the 1951 to 1955 cohort. At ages 51 to 55, the likelihood of owning a home fell from
80 percent for the 1931 to 1935 birth cohort to 67 percent for the 1961 to 1965 cohort. And at
ages 41 to 45, the likelihood of homeownership fell from 75 percent for the 1931 to 1935 birth
cohort to 59 percent for the 1971 to 1975 cohort. Homeownership has stabilized at younger ages
for people born between 1961 and 1980. However, it slipped again for Millennials. At ages 26
to 30, only 28 percent of those born between 1981 and 1985 and 24 percent of those between
1986 and 1990 owned a home, compared with 35 percent of those born between 1976 and 1980
and 47 percent of those born between 1951 and 1955. This long-term decline in homeownership
predated the Great Recession and related financial crisis.
15
Household Net Worth
Figure 14 reports median per capita household net worth, in constant 2015 dollars, by six-
year age and birth cohort groups. The SCF data used to compute these estimates cover 1989 to
2013. The most noteworthy feature of the chart is the decline in net worth following the 2007
collapse in housing values, the 2008 stock market crash, and the Great Recession, as can be seen
by the drop at the end of each line in the figure. Between 2007 and 2013, median net worth fell
between 16 and 28 percent for each cohort between the ages of 38 to 43 and 68 to 73 in 2007.
Younger people, who did not have much to lose, and older people, who were less likely
to have been heavily invested in equities or to have been laid off, lost less. Median household
net worth remained virtually unchanged between 2007 and 2013 for people born between 1970
and 1975, who were ages 32 to 37 in 2007, and grew 32 percent for people born between 1976
and 1981, who were ages 26 to 31. Nonetheless, the economic downturn clearly harmed these
younger people, who otherwise would have experienced strong growth in household wealth.
Median household net worth increased 122 percent over the next six years for the 1964 to 1969
cohort at ages 32 to 37 and roughly doubled over the next six years for the 1970 to 1975 cohort
and the 1964 to 1969 cohorts at ages 26 to 31.
Disregarding 2013 data because of the anomalies created by the financial crisis, we see
that household wealth increased rapidly through about one’s early 60s and that each successive
cohort born in the 1930s, 1940s, 1950s, and early 1960s generally had more net worth than the
preceding cohort. At ages 56 to 61, for example, median household net worth was 168 percent
higher for the 1946 to 1951 birth cohort than the 1928 to 1933 birth cohort, born 18 years earlier.
However, growth in household net worth has been slowing. Median net worth at ages 38 to 43
was only 17 percent higher for those in the 1964 to 1969 birth cohort than those born 18 years
earlier, and at ages 32 to 37 median net worth was lower for those in the 1970 to 1975 cohort
than those born 18 years earlier.
The financial crisis and Great Recession wiped out these gains. Between October 2007
and February 2009, the S&P 500 index lost 53 percent of its value. Five years after the 2008
stock market crash, each cohort born after 1945 had lower median net worth than the preceding
cohort had at the same age, even though the S&P 500 index exceeded its October 2007 value by
March 2013 (and has continued to grow). Unless younger generations can recover, these losses
could threaten future retirement security.
16
Financial Wealth
Across all cohorts, the likelihood of having financial wealth increased with age, and
nearly all adults—about 95 percent—held some financial assets by the time they reached their
40s (Figure 15). The share of adults with some financial wealth increased with each successive
cohort up to those born between 1952 and 1957, and then leveled off, although those born
between 1964 and 1981 were more likely to hold financial assets at ages 26 to 31 than the late
Boomers born between 1958 and 1963.
Median inflation-adjusted financial wealth for adults with holdings generally increased
through 2007 with each succeeding cohort until the 1958 to 1963 cohort (Figure 16). At ages 50
to 55, for example, median financial wealth was about twice as high for those in the 1952 to
1957 cohort as for those in the 1934 to 1939 cohort. Later cohorts have fallen behind their
predecessors. Median financial wealth at ages 38 to 43 was lower for the 1964 to 1969 cohort
than for those born six years earlier, and median financial wealth at ages 32 to 37 was lower for
the 1970 to 1975 cohort than for the previous six-year cohort. These two cohort comparisons
related wealth levels in 2007 and 2001. Outcomes for later cohorts appeared more ominous
when we incorporated into the analysis 2013 data, which included the effects of the financial
crisis and Great Recession. Median 2013 financial wealth for each cohort born after 1945 was
lower than for the previous cohort at the same age, often by a substantial amount. Median
financial wealth was 72 percent lower in 2013 than 2007 at ages 50 to 55, for example, and 47
percent lower at ages 26 to 31.
The outlook for the most recent generation appeared somewhat more promising when we
considered only retirement account holdings. The share of adults holding a retirement account
generally increased with each successive birth cohort, peaking at 70 percent in the 1952 to 1957
birth cohort, although retirement account ownership has declined recently (Figure 17). The most
recent birth cohorts were somewhat less likely to hold a retirement account after age 31 than
those in the 1958 to 1963 cohort. Moreover, the share with an account fell between 2007 and
2013 for people ages 44 and older. An encouraging sign, however, is that median account
balances have been increasing steadily over time, and the median balance for members of the
1976 to 1981 cohort with accounts was higher that the median balance for their counterparts in
earlier cohorts at the same age (Figure 18). Median account balances were higher in 2013 than
2007. These results are generally consistent with data from IRA administrators and retirement
17
plan recordkeeping organizations, which show that median IRA balances increased 31 percent
from 2010 to 2014 (Copeland, 2017), while median 401(k) balances fell by about one-third
between 2007 and 2008 before rebounding in 2013 to about their 2007 levels (VanDerhei et al.,
2017).
Housing Wealth
Homeownership was more common in our SCF sample than in our CPS/ASEC sample,
because we restricted our SCF sample to household heads and their spouses, excluding adults
who had not established their own households (whom we included as non-homeowners in our
CPS/ASEC sample). Our SCF homeownership rates did not vary much by cohort, although they
generally fell between 2007 and 2013 for people younger than traditional retirement age (Figure
19). Nearly 8 in 10 household heads and their spouses ages 44 to 49 were homeowners in 2007.
Six years later, only 3 in 4 those ages 50 to 55 were homeowners.
Among homeowners younger than 62 in 2001, median housing wealth surged between
2001 and 2007 and then plunged between 2007 and 2013, the endpoint of each line in Figure 20.
For all homeowner cohorts, however, median inflation-adjusted housing wealth was higher in
2013 than 2001. For the 1952 to 1957 birth cohort, median housing wealth among homeowners
increased 79 percent from 2001 to 2007 and fell 24 percent between 2007 and 2013. Through
2007, housing wealth increased rapidly with each successive cohort. At ages 44 to 49, median
wealth was 64 percent higher for the 1958 to 1963 birth cohort than the cohort born six years
earlier, partly because the mid-2000s housing bubble boosted home values for the later cohort.
Even before the housing bubble, however, the 1958 to 1963 cohort had substantially more
housing wealth than the previous cohort. After the collapse in housing prices after 2007, median
housing wealth for cohorts after 1945 fell below the inflation-adjusted median for the cohort
born six years earlier but remained above the median for the cohort born 12 years earlier. Most
of these gains, however, were concentrated among homeowners living in and around major cities
on the East and West Coasts.
Household Debt
The growth in household debt is one of the most striking changes in household finances
over the past quarter-century. The share of household heads and spouses with debt has not
18
changed much at midlife or younger ages, although it has fallen somewhat over the past two
decades for people their 20s, 30s, and early 40s (Figure 21). Debt has increased much more at
older ages. At ages 62 to 67, 76 percent of those in the 1940 to 1945 cohort held debt, compared
with 64 percent of those in the 1928 to 1933 cohort.
Median debt holdings among people with debt has risen sharply over time. At ages 50 to
55, median debt for debtholders was $62,200 in the 1952 to 1957 cohort, compared with only
$18,700 for debtholders in the 1940 to 1945 cohort, 12 years earlier (Figure 22). Median debt
peaked at ages 38 to 43 at $77,500 for adults in the 1964 to 1969 birth cohort, nearly twice as
high as the median at that age for adults in the 1952 to 1957 cohort. Increases in housing prices,
financial innovations that increased access to credit, demographic shifts, and low and stagnating
incomes spurred the rise in household indebtedness (Barba and Pivetti, 2009; Dynan and Kohn,
2007). Student loan debt has also been growing (Brown et al., 2014). Both the likelihood of
holding debt and the median debt level for debtholders declined sharply between 2007 and 2013.
However, debt levels have been rising since 2013 and reached an all-time high in the second
quarter of 2017 (Federal Reserve Bank of New York, 2017). As people enter retirement with
more debt, debt service payments could strain their financial resources (Butrica and Karamcheva,
2013; Karamcheva, 2013).
Retirement Income Projections
Turning to future retirement income, we estimated that median per capita family income
at age 70 will generally increase over time. For both men and women, DYNASIM4 projected
that median traditional income at age 70—the cash income measure used by the Census
Bureau—will be higher for the 1946 to 1955 cohort than the previous 10-year cohort, which
turned age 70 between 2006 and 2015 (Table 1). Median age-70 incomes were projected to fall
for the 1956 to 1965 birth cohort—the late baby boomers—but then rise steadily over time. The
projected increases depended on assumptions about future wage growth. When we followed the
Social Security trustees’ optimistic annual wage growth assumption of 1.2 percent—our high
wage-growth assumption—we projected that median per capita age-70 income for men and
women combined will reach $38,000 in 2015 inflation-adjusted dollars for the late Gen-Xers and
early Millennials, born between 1976 and 1985. This projection is 15 percent higher than the
projection for those born 40 years earlier (between 1936 and 1945). When we assumed more
19
moderate wage growth of 0.81 percent per year, DYNASIM4 projected that median per capita
age-70 income would be $36,000 for the late Gen-Xers and early Millennials, 9 percent more
than for those born 40 years earlier. Projected median per capita age-70 income for the late Gen-
Xers and early Millennials fell to $35,000, 6 percent more than for those born 40 years earlier,
when we assumed relatively slow wage growth of 0.70 percent per year.
Patterns were similar when we compared age-70 incomes across cohorts using the total
potential income measure. This measure included the annuitized value of 80 percent of
household financial assets and better reflects the impact on financial security of the shift from
DB pensions to DC retirement accounts. Overall median age-70 income was 8 percent higher
using the total potential income measure than the traditional cash measure under the moderate-
wage-growth assumption and 13 percent higher under the high-wage-growth assumption.
Compared with the 1936 to 1945 cohort, median total potential income was 16 percent for the
1976 to 1985 cohort under the high-wage-growth assumption, 5 percent higher under the
moderate-wage-growth assumption, and 3 percent higher under the low-wage-growth
assumption. However, under the low- and moderate-wage-growth assumptions, median age-70
incomes for the 1946 to 1955 cohorts exceeded those for the 1976 to 1985 cohorts.
If real average wages grow 1.2 percent per year, we projected that median inflation-
adjusted annuitized income at age 70 will be 8 percent higher for the 1976 to 1985 birth cohort
than for the 1946 to 1955 cohort. If real average wages instead grow at 0.82 percent per year,
median inflation-adjusted annuitized income at age 70 will be 3 percent higher for the 1976 to
1985 birth cohort than for the 1946 to 1955 cohort.
Projected median per capita family income at age 70 grew faster for women than men,
especially for the traditional cash income measure, reflecting the growth in women’s earnings.
Compared with the 1936 to 1945 cohort, projected median traditional cash income in the 1976 to
1985 cohort under the moderate-wage-growth assumption was 13 percent higher for women but
only 3 percent higher for men. However, for each cohort median age-70 income was higher for
men than women for both income measures under all wage-growth assumptions.
For lower- and middle-income retirees, much of the projected income increase came from
higher Social Security incomes. For 70-year-olds in the middle income quintile, projected mean
per capita income in the 1976 to 1985 birth cohort was $2,400 higher than in the 1936 to 1945
cohort, after we adjusted for inflation (Table 2). Projected mean income grew $5,700 for Social
20
Security, $2,400 for income from financial assets, and $1,000 for earnings, while mean DB
pension income fell $6,700. For adults in the bottom income quintile, mean Social Security
income grew $1,800 over the period, while a $700 average gain in income from financial assets
was exactly offset by losses in average DB pension income and SSI. Earnings growth accounted
for most of the projected age-70 income gains from the 1936 to 1945 cohort to the 1976 to 1985
cohort for adults in the top income quintile. Their mean DB pension income fell $20,800.
Although we projected that median age-70 incomes will rise in the coming decades, we
projected that the share of 70-year-olds able to replace at least 75 percent of the average annual
earnings they received between ages 50 and 54—a common rule of thumb for retirement income
adequacy—will decline (Table 3). According to DYNASIM4 projections that used the
traditional income measure and moderate wage growth assumptions, the share of 70-year-olds
with a replacement rate below 75 percent will increase from 38 percent for the 1936 to 1945
birth cohort to 44 percent for the 1966 to 1975 cohort and 46 percent for the 1976 to 1985 cohort.
The share with potentially inadequate retirement incomes was somewhat lower when the
projections used the high wage-growth assumption—44 percent for the 1976 to 1985 cohort—
and somewhat higher when they used the low wage-growth assumption—47 percent for the 1976
to 1985 cohort.
We projected that more older adults would be able to replace 75 percent of their pre-
retirement earnings if they annuitized their financial assets, but the projected share likely to
experience lower living standards in retirement than while working again increased over time.
Using the total potential income measure, we estimated that 40 percent of adults in the 1976 to
1985 cohort would fall short of a 75 percent replacement rate at age 70 if wages grew moderately
over time, up from 32 percent for the 1936 to 1945 cohort and 30 percent for the 1956 to 1965
cohort. The corresponding share for the 1976 to 1985 cohort was 38 percent under the high
wage-growth assumption and 41 percent under the low wage-growth assumption.
Conclusion
How future generations fare in retirement will depend largely on how much they earned
and saved when they were younger. Many recent employment, earnings, and savings trends are
discouraging. Men’s labor force participation rates prior to age 55 continue to decline, and their
median wages have been stagnant for decades. Full-time male workers born in the late 1970s
21
and early 1980s were less likely to participate in an employer-sponsored retirement plan than
previous cohorts, and their female cohorts were less likely to participate than those born a decade
earlier. Adults younger than traditional retirement ages have accumulated less wealth than
previous cohorts. Other trends have been more encouraging, however. Gen-X and Millennial
women worked and earned more in their 20s and 30s than now-retired women did at those ages.
And Millennial men and women are much more likely to have a four-year college degree than
previous cohorts.
Our projections showed that median, age-70 income will be higher for Gen-Xers and
Millennials than previous generations, but they face a higher risk of seeing their living standards
fall when they retire. Using a measure of retirement income that includes payouts that could be
collected from an actuarially fair annuity valued at 80 percent of a retiree’s financial assets and
retirement accounts, we found that 40 percent of 70-year-olds born between 1976 and 1985
would be unable to replace at least 75 percent of the inflation-adjusted average annual earnings
that they and their spouse received from ages 50 to 54, under the assumption that average wages
grow at the same rate as they did between 1966 and 2015. By comparison, replacement rates at
age 70 would likely fall short of the 75 percent threshold for 32 percent of those born between
1936 and 1945, and for 30 percent of those born between 1956 and 1965.
Retirement is still more than three decades away for most people born in the 1980s, and
their financial security in old age will hinge on several factors that have yet to play out. The
future course of stock market returns, interest rates, housing prices, and inflation will affect
future retirement incomes. How long people tend to work, which will depend partly on how
health trajectories evolve, will help determine financial security for future retirees. Policy
choices regarding retirement programs, especially Social Security, will play a role. Our
projections assumed that Social Security will pay all future retirees the benefits that they are
scheduled to receive under current rules. However, because Social Security faces a long-term
financing shortfall, the program’s trustees project that within two decades it will be able to pay
only about three-quarters of scheduled benefits (Board of Trustees, Federal Old-Age and
Survivors Insurance and Federal Disability Insurance Trust Funds, 2017). If Congress cuts
benefits to close the funding gap, or does nothing and allows benefits to fall by about one-
quarter, future retirement incomes will be much lower than we projected.
22
How rapidly future wages grow will also shape future retirement security. Wage growth
will depend on labor productivity, which will likely continue to rise, although perhaps more
slowly than in the past (Fernald, 2016; Gordon, 2014). However, the relationship between wage
growth and labor productivity growth has been weakening, reducing the share of the nation’s
output that goes to labor. Over the past decade, productivity in the non-farm business sector
increased 12.3 percent, while real compensation of labor increased only 5.1 percent (Solow,
2015). Declining unionization, the shift from labor to capital, and rising employer health care
costs may explain why wages have not been keeping pace with productivity growth
(Congressional Budget Office, 2016; Ginsburg, 2014; Karabarbounis and Neiman 2013).
Rising out-of-pocket spending on health care and long-term services and supports (LTSS)
poses an additional threat to future retirees’ financial security. Although Medicare covers nearly
all older adults, out-of-pocket spending on Medicare premiums, premiums for supplemental
private insurance, copays, and uncovered services can be financially burdensome. Hatfield et al.
(2016) project that between 2012 and 2030, the median share of income that adults ages 65 and
older spend on medical services will increase from 10 to 14 percent. Fronstin and VanDerhei
(2017) estimate that a 65-year-old man would need $127,000 in savings to be 90 percent certain
of covering all future medical expenses, and a 65-year-old woman would need $143,000.
Spending on LTSS, which includes nursing home care, residential care, and home care, can be
even more burdensome for families because Medicare does not usually cover them, relatively
few people have private long-term care insurance, and Medicaid pays only for people who have
already depleted virtually all their wealth. Favreault and Dey (2015) project that people turning
65 today need to set aside $36,000 by age 65 to cover expected lifetime out-of-pocket costs for
intensive LTSS, and about 1 in 10 will need to set aside more than $100,000.
Our analysis focused on median outcomes, describing the employment, earnings, and
wealth of the “typical” worker and the income that the “typical” future retiree is likely to receive.
However, medians mask important differences across population subgroups. Employment,
earnings, and household wealth vary substantially by educational level, race and ethnicity,
household structure, immigrant status, and other factors (Autor, 2014; Cobb-Clark and
Hildebrand, 2006; Fisher and Houseworth, 2017; Hirsch and Winters, 2014; McKernan et al.,
2014). Consequently, future retirement income for people with limited education, people of
23
color, and people who spent much of their lives outside of marriage may differ significantly from
the overall median outcome. Future research should explore these distributional differences.
24
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33
Figure 1. Percentage of Men Ages 26 to 30 with a Four-Year College Degree, by Birth Cohort
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 2. Percentage of Women Ages 26 to 30 with a Four-Year College Degree, by Birth Cohort
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
1720
2825 24 24
27 2826
29
34
0
5
10
15
20
25
30
35
40
Birth cohort
1113
20 20 22 2328
32 3337
40
05
1015202530354045
Birth cohort
34
Figure 3. Men’s Labor Force Participation Rates, by Age and Birth Cohort (%)
Note: The labor force participation rate is the share of the civilian noninstitutionalized population that is working or looking for work. Appendix Table 1 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 4. Women’s Labor Force Participation Rates, by Age and Birth Cohort (%)
Note: The labor force participation rate is the share of the civilian noninstitutionalized population that is working or looking for work. Appendix Table 2 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
0%
25%
50%
75%
100%
Age
1931-35 1941-451951-55 1961-651971-75 1976-801981-85 1986-90
Cohort
0%
25%
50%
75%
100%
Age
1931-351941-451951-551961-651971-751976-801981-851986-90
Cohort
35
Figure 5. Men’s Full-Time Employment Rates, by Age and Birth Cohort (%)
Note: The figure shows the percentage of civilian noninstitutionalized men employed full time. Appendix Table 3 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 6. Women’s Full-Time Employment Rates, by Age and Birth Cohort (%)
Note: The figure shows the percentage of civilian noninstitutionalized women employed full time. Appendix Table 4 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
0%
25%
50%
75%
100%
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
0%
25%
50%
75%
100%
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
36
Figure 7. Men’s Median Inflation-Adjusted Earnings, Full-Time Workers, by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars. Appendix Table 5 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 8. Women’s Median Inflation-Adjusted Earnings, Full-Time Workers, by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars. Appendix Table 6 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
37
Figure 9. Percentage of Full-Time Male Workers Participating in an Employer-Sponsored Retirement Plan, by Birth Cohort
Note: The figure excludes 2016 data, because the CPS/ASEC appears to have underestimated retirement plan coverage since 2014. Appendix Table 7 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 10. Percentage of Full-Time Female Workers Participating in an Employer-Sponsored Retirement Plan, by Birth Cohort
Note: The figure excludes 2016 data, because the CPS/ASEC appears to have underestimated retirement plan coverage since 2014. Appendix Table 8 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
0%
10%
20%
30%
40%
50%
60%
70%
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
0%
10%
20%
30%
40%
50%
60%
70%
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
38
Figure 11. Percentage of Men Married, by Age and Birth Cohort
Note: Appendix Table 9 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 12. Percentage of Women Married, by Age and Birth Cohort
Note: Appendix Table 10 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
0%
25%
50%
75%
100%
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
0%
25%
50%
75%
100%
Age
1931-35 1941-45 1951-55 1961-651971-75 1976-80 1981-85 1986-90
Cohort
39
Figure 13. Percentage of Adults Who Own a Home, by Age and Birth Cohort
Note: Appendix Table 11 provides additional data. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Figure 14. Median Per Capita Inflation-Adjusted Household Net Worth by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars. The analysis divided household net worth by two for married adults. Appendix Table 12 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013.
$0
$50,000
$100,000
$150,000
$200,000
Age
1928-1933 1934-19391940-1945 1946-19511952-1957 1958-19631964-1969 1970-19751976-1981
Cohort
0%
25%
50%
75%
100%
Age
1931-35 1941-451951-55 1961-651971-75 1976-801981-85 1986-90
Cohort
40
Figure 15. Share of Adults with Financial Assets, by Age and Birth Cohort
Note: Estimates include the value of retirement accounts. Appendix Table 13 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013. Figure 16. Median Per Capita Inflation-Adjusted Value of Financial Assets for Adults with Positive Holdings, by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars and include the value of retirement accounts. The analysis divided the value of financial assets by two for married adults. Appendix Table 14 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013.
0%
25%
50%
75%
100%
Age
1928-1933 1934-1939 1940-19451946-1951 1952-1957 1958-19631964-1969 1970-1975 1976-1981
Cohort
$0
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Age
1928-1933 1934-19391940-1945 1946-19511952-1957 1958-19631964-1969 1970-19751976-1981
Cohort
41
Figure 17. Share of Adults with Retirement Accounts, by Age and Birth Cohort
Note: Appendix Table 15 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013. Figure 18. Median per Capita Inflation-Adjusted Value of Retirement Accounts for Accountholders, by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars. The analysis divided the value of retirement accounts by two for married adults. Appendix Table 16 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013.
0%
20%
40%
60%
80%
Age
1928-1933 1934-1939 1940-19451946-1951 1952-1957 1958-19631964-1969 1970-1975 1976-1981
Cohort
$0
$30,000
$60,000
$90,000
Age
1928-19331934-19391940-19451946-19511952-19571958-19631964-19691970-19751976-1981
Cohort
42
Figure 19. Share of Adults with Housing Wealth, by Age and Birth Cohort
Note: Estimates refer to gross housing wealth. Net housing wealth, or home equity, was negative for people whose housing debt exceeded their housing wealth. Appendix Table 17 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013. Figure 20. Median Per Capita Inflation-Adjusted Value of Housing Wealth for Adults with Housing Wealth, by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars. The analysis divided housing wealth by two for married adults. Estimates refer to gross housing wealth. Net housing wealth, or home equity, was negative for people whose housing debt exceeded their housing wealth. Appendix Table 18 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013.
0%
25%
50%
75%
100%
Age
1928-1933 1934-19391940-1945 1946-19511952-1957 1958-19631964-1969 1970-19751976-1981
Cohort
$0
$100,000
$200,000
$300,000
Age
1928-19331934-19391940-19451946-19511952-19571958-19631964-19691970-19751976-1981
Cohort
43
Figure 21. Share of Adults with Outstanding Debt, by Age and Birth Cohort
Note: Appendix Table 19 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013. Figure 22. Median per Capita Inflation-Adjusted Value of Outstanding Debt for Debtholders, by Age and Birth Cohort
Note: Estimates are reported in constant 2015 dollars. The analysis divided household debt by two for married adults. Appendix Table 20 provides additional data. Source: Authors’ tabulations from the Survey of Consumer Finances, 1983-2013.
0%
25%
50%
75%
100%
Age
1928-1933 1934-19391940-1945 1946-19511952-1957 1958-19631964-1969 1970-19751976-1981
Cohort
$0
$20,000
$40,000
$60,000
$80,000
Age
1928-19331934-19391940-19451946-19511952-19571958-19631964-19691970-19751976-1981
Cohort
44
Table 1. Projected Median Per Capita Inflation-Adjusted Annual Family Income at Age 70 by Birth Cohort, Sex, and Wage Growth Assumption
1936-45 1946-55 1956-65 1966-75 1976-85 Traditional Income Measure Men and Women High wage growth (1.2 percent per year) 33,000 35,000 33,000 36,000 38,000 Moderate wage growth (0.81 percent per year) 33,000 35,000 33,000 34,000 36,000 Low wage growth (0.70 percent per year) 33,000 35,000 33,000 34,000 35,000 Men High wage growth (1.2 percent per year) 36,000 37,000 36,000 38,000 40,000 Moderate wage growth (0.81 percent per year) 36,000 37,000 35,000 36,000 37,000 Low wage growth (0.70 percent per year) 36,000 37,000 35,000 36,000 36,000 Women High wage growth (1.2 percent per year) 30,000 33,000 31,000 34,000 37,000 Moderate wage growth (0.81 percent per year) 30,000 33,000 31,000 32,000 34,000 Low wage growth (0.70 percent per year) 30,000 33,000 30,000 32,000 33,000 Total Potential Income Measure Men and Women High wage growth (1.2 percent per year) 37,000 40,000 38,000 40,000 43,000 Moderate wage growth (0.81 percent per year) 37,000 40,000 37,000 38,000 39,000 Low wage growth (0.70 percent per year) 37,000 40,000 37,000 37,000 38,000 Men High wage growth (1.2 percent per year) 40,000 42,000 41,000 43,000 46,000 Moderate wage growth (0.81 percent per year) 40,000 42,000 40,000 41,000 42,000 Low wage growth (0.70 percent per year) 40,000 42,000 40,000 40,000 41,000 Women High wage growth (1.2 percent per year) 34,000 38,000 35,000 37,000 40,000 Moderate wage growth (0.81 percent per year) 34,000 38,000 35,000 35,000 37,000 Low wage growth (0.70 percent per year) 34,000 38,000 34,000 35,000 36,000
Note: Estimates are reported in constant 2015 dollars and rounded to the nearest $1,000. The traditional income measure includes earnings, Social Security, DB pensions, SSI, interest, dividends, rent, and occasional withdrawals from retirement accounts. The total potential income measure includes earnings, Social Security, DB pensions, SSI, and the annual income from an actuarially fair annuity valued at 80 percent of financial assets, including retirement accounts. The analysis divides total household income by two for married adults. Source: Dynamic Simulation of Income Model 4, Run Numbers 942B, 942D, and 942E.
45
Table 2. Projected Mean Per Capita Inflation-Adjusted Annual Family Income at Age 70, by Income Type, Income Quintile, and Birth Cohort 1936-45 1976-85 Difference Bottom income quintile Social Security income 8,300 10,100 1,800 Earnings 500 500 0 Income from financial assets 800 1,500 700 DB pension income 600 200 -400 SSI 500 200 -300 Total 10,700 12,500 1,800 Middle income quintile Social Security income 15,800 21,500 5,700 Earnings 6,000 7,000 1,000 Income from financial assets 7,100 9,500 2,400 DB pension income 7,800 1,100 -6,700 SSI 0 0 0 Total 36,700 39,100 2,400 Top income quintile Social Security income 18,000 26,600 8,600 Earnings 34,100 77,700 43,600 Income from financial assets 47,900 47,500 -400 DB pension income 25,500 4,700 -20,800 SSI 0 0 0 Total 125,500 156,500 31,000 Note: Estimates are reported in constant 2015 dollars and rounded to the nearest $100. The tables shows mean total potential income, which includes earnings, Social Security, DB pensions, SSI, and the annual income from an actuarially fair annuity valued at 80 percent of financial assets, including retirement accounts. The analysis divides total household income by two for married adults. The projections assumed that future wage growth averaged 0.81 percent per year. Source: Dynamic Simulation of Income Model 4, Run Numbers 942B, 942D, and 942E.
46
Table 3. Percentage of Adults Whose Projected Age-70 Income Falls Short of a 75 Percent Replacement Rate, by Birth Cohort, Income Measure, and Wage Growth Assumption
1936-45 1946-55 1956-65 1966-75 1976-85 Traditional income measure High wage growth (1.2 percent per year) 38 39 36 42 44 Moderate wage growth (0.81 percent per year) 38 40 37 44 46 Low wage growth (0.70 percent per year) 38 39 37 45 47 Total potential income measure High wage growth (1.2 percent per year) 32 33 30 37 38 Moderate wage growth (0.81 percent per year) 32 33 30 38 40 Low wage growth (0.70 percent per year) 32 33 30 39 41
Note: Estimates are reported in constant 2015 dollars and rounded to the nearest $1,000. The traditional income measure includes earnings, Social Security, DB pensions, SSI, interest, dividends, rent, and occasional withdrawals from retirement accounts. The total potential income measure includes earnings, Social Security, DB pensions, SSI, and the annual income from an actuarially fair annuity valued at 80 percent of financial assets, including retirement accounts. The analysis divides total household income by two for married adults. Source: Dynamic Simulation of Income Model 4, Run Numbers 942B, 942D, and 942E.
47
Appendix
This appendix describes how DYNASIM4 projects financial outcomes.
Employment and Earnings
DYNASIM projects the likelihood that an individual works each year as a function of
age, sex, race/ethnicity, education, health and disability status, geographic region, marital status,
student status, number of young children, spouse characteristics (employment, age, disability,
and education), immigrant status, Social Security benefit status, cohort, and the state-specific
unemployment rate. The likelihood also includes an estimated individual-specific error term that
captures nonvarying individual preferences that are independent of observed characteristics. The
model classifies an individual as employed if his or her expected probability of working exceeds
a given random number. The selection criteria are adjusted so that our employment projections
for men and women within particular age groups hit the trustees’ targets.1
DYNASIM uses a similar set of explanatory variables to assign hourly wages and annual
hours of work to those projected to work in the calendar year. Annual earnings are computed as
the product of the hourly wage and annual hours worked. DYNASIM adjusts the underlying
predicted annual wage for real wage growth based on the trustees’ economic assumptions. It
also aligns the annual earnings of workers to hit the Social Security trustees’ annual earnings
targets. The model has a special projection procedure for very high earners—those in the top
one-tenth of 1 percent—because of the relatively high share of aggregate earnings that such
earners garner.
The underlying price and wage targets from the Social Security trustees affect various
other projections, including the Social Security wage base (the taxable maximum), the indexing
of wages for the calculation of Social Security benefits, SSI benefit parameters, stock and bond
rates of return, and interest rates. Changes in economic conditions also affect retirement and
Social Security benefit claiming, as do marriage, divorce, fertility, and schooling outcomes.
Income and Payroll Tax
DYNASIM calculates federal income tax liabilities by using an income tax calculator
developed by Jon Bakija (Smith et al., 2007). The tax calculator uses annual projected tax unit
1 The random error term follows an autoregressive process with a one-year lag so that random shocks include both new and lagged effects.
48
income and assets from the SIPP panels matched to a Statistics of Income (SOI) data file that
includes itemized deductions and other variables needed to calculate income tax. The tax
calculator assumes current-law federal income tax rules, including the provisions in the
American Tax Relief Act of 2012. Tax provisions affecting the treatment of Social Security
benefits have not changed since 1993, but the share of Social Security benefits included in
taxable income is continually increasing under current law partly because the threshold levels for
including benefits in taxable income are not indexed for inflation. Other than the Social Security
thresholds, DYNASIM inflates thresholds by projected changes in the consumer price index.
DYNASIM also calculates Social Security coverage and annual payroll taxes by using current-
law payroll tax rates. Only earnings in Social Security-covered employment are subject to
payroll taxes.
Retirement Accounts
DYNASIM projects retirement accounts based on annual contributions to investment
accounts and accumulated investment returns. DYNASIM starts with the self-reported SIPP
retirement account balances. Because of documented deficiencies in the SIPP asset data (Czajka,
Jacobson, and Cody, 2003; Smith, Favreault, and Cashin, 2005), asset balances in retirement
accounts—as well as financial assets outside of retirement accounts—in DYNASIM’s starting
SIPP sample are adjusted to align with asset distributions from the 2007 SCF.
DYNASIM grows stock, long-term corporate bond, and long-term government bond
portfolios by using historical price changes and returns through 2015. Investment experience
varies for each individual because the model sets rates of return stochastically, using historical
means and standard deviations. The model accounts for the 2008 stock market crash, which
reduced equity values by 37 percent, by assuming the market recovers to half its projected
precrash value by 2017 (Butrica, Smith, and Toder, 2009, 2010). DYNASIM implements this
assumption by using historic returns through 2015 and assumes a 7.42 percent average real rate
of return on stocks from 2016 to 2017 before stocks resume their historic average real return of
6.5 percent after 2017. DYNASIM assumes mean real rates of return of 3.5 percent for
corporate bonds, 3.0 percent for government bonds, and standard deviations of 17.28 percent for
49
stocks and 2.14 percent for bonds.2 The 6.5 percent real return on stocks reflects a capital
appreciation of about 3.5 percent and a dividend yield of around 3.0 percent, in line with the
long-term performance of the S&P 500. The model subtracts one percentage point from annual
stock and bond returns to reflect administrative costs.
Individuals are assigned an individual-specific risk tolerance based on SCF data. An
individual’s share of retirement account assets invested in equities varies by age and risk
tolerance, with high-risk and younger individuals investing more in equities than low-risk and
older individuals.
DYNASIM assigns a growing share of workers to invest in target-date funds over time by
using prevalence rates from the Employee Benefits Research Institute (Copeland, 2011).
DYNASIM assigns target-date investors to specific target-date funds based on the dollar-
weighted share of the 40 largest target-date funds (Morningstar, 2012, table 3). Workers with
target-date funds use the stock and bond portfolio mix of their assigned fund at each age. All
investors rebalance portfolios annually to preserve the target mix of stocks and bonds.
DYNASIM assumes 40 percent of firms offering DC plans implement automatic
enrollment beginning in 2008. Automatic enrollment increases the probability new hires will
participate in DC plans in the first year on the job, but workers can still opt out. Automatic
enrollment affects new hire participation, but the participation probability is higher for workers
who contributed in a prior year, so automatic enrollment increases participation on average
beyond the first year on the job.
DYNASIM allows some workers to cash out retirement account balances with job
changes or job losses. Younger workers, workers with lower account balances, and workers who
lose their jobs are more likely to cash out retirement account balances than are older workers,
those with higher balances, and those who move from one job to another without a break in
employment. High unemployment contributes to lower lifetime DC pension savings through
workers’ hardship withdrawals and loss of contributions (and lost returns on those lost
contributions) when out of work.
2 The standard deviations are derived from real returns over the 58-year period between 1952 and 2010 for large-company stocks and Treasury bills as reported in Ibbotson Associates (2014). Inflation assumptions follow the 2016 intermediate assumptions used by the Social Security trustees (Board of Trustees, Federal Old-Age and Survivors Insurance and Federal Disability Insurance Trust Funds, 2016).
50
DYNASIM’s pension projections allow the user to select alternate future pension
assumptions including a more rapid shift from DB pensions to DC pensions as well as
expansions of DC plans to firms not currently offering pensions (Butrica and Smith, 2016).
Users can specify investment choice (Roth IRA, traditional IRA, 401k), firm size thresholds,
default investment rate, share of participants that select the default investment, portfolio
allocation (target-date fund, Treasury G fund, blend of stocks and bonds).
Financial Assets
DYNASIM uses random-effects models developed for the Social Security
Administration’s (SSA) MINT model to project financial assets. DYNASIM starts with SIPP
self-reported assets (saving, checking, and money market accounts; certificates of deposit;
stocks; bonds; and equity in businesses, vehicles, and nonhome real estate, less unsecured debt).
As with retirement accounts, we adjust the SIPP starting values to align with the household asset
distribution from the 2007 SCF.
Financial assets accumulate and decumulate as a function of family characteristics and
earnings and projected wage differentials. The main economic explanatory variable is the
individual’s lifetime earnings relative to the cohort average. Individuals with above-average
lifetime earnings accumulate assets faster than those with below-average lifetime earnings. A
spell of unemployment will lower a worker’s average compared with a worker who remains
employed continuously. The longer the unemployment spell, the greater is the differential in
lifetime earnings relative to the cohort average, and the greater is the impact on projected assets.
Assets accumulate at the family level, so husbands and wives equally share family assets. We
assume couples split assets at divorce and survivors inherit the assets of deceased spouses.
DYNASIM projects nonpension financial assets over three age ranges: up to age 50, from
51 to retirement, and from retirement to death. Equations projecting assets to age 50 were
estimated based on the PSID (Toder et al., 2002). Equations projecting assets from age 51 to
retirement were estimated on the first seven waves of the Health and Retirement Study (HRS)
(Smith et al., 2007). Equations projecting assets from retirement to death were estimated on a
synthetic panel of SIPP data (Toder et al., 1999). The latter two datasets include historic
earnings from the SSA’s summary earnings records data.
51
Asset decumulation includes simulated sharp reductions in assets associated with health
shocks, institutionalization, and end of life.
Homeownership
DYNASIM starts with self-reported homeownership status. Nonhomeowners are subject
to an annual home purchase logistic hazard model. Homeowners are subject to an annual home
sale logistic hazard model. The home purchase and sale hazard models were estimated on 1984-
2013 PSID from age 25 until death. The home purchase model includes age, lifetime earnings,
recent earnings, marital status, education, and number of children. It also included the ratio of
median state home value to national median home value and an indicator for whether the family
lives in metro area with 1 million or more people (defined in 2014 American Community Survey
data). The home sale model includes age, sex, lifetime earnings, recent earnings, age, marital
status, divorce duration, first child indicator, and number of children under age 18.
Home Equity
As with financial assets, DYNASIM uses random-effects models developed for the
SSA’s MINT model to project home equity among homeowners. DYNASIM starts with SIPP
self-reported home equity. These models project home equity over three age ranges: 25 to 50, 51
to 70, and 71 to death. Equations projecting home equity to age 50 were estimated based on the
PSID (Toder et al., 2002). Equations projecting equity from age 51 to 70 were estimated based
on the first seven waves of the HRS (Smith et al., 2007). DYNASIM holds real home equity
constant after age 70 for individuals who continue to own their homes.
Retirement Income
DYNASIM projects income from various other sources to generate a measure of total
household income. Social Security income is computed based on the benefit formula, projected
lifetime earnings, marriage history, and an equation projecting benefit take-up. DYNASIM
projects retirement plan coverage and participation, plan contributions, and payments from
employer-sponsored DB pension plans, cash balance plans, and retirement accounts based on
equations of job change. DYNASIM also projects asset income as a function of projected assets.
52
Finally, DYNASIM calculates SSI for eligible individuals based on total family income, assets,
and state-specific program rules.
Pensions
DYNASIM projects pensions from employer-sponsored DB plans, cash balance plans,
and retirement accounts, including 401(k) and 403(b) plans, Keoghs, and IRAs. Starting
information about pension coverage on current and past jobs, pension contribution rates, and
account balances comes from SIPP self-reported information. DYNASIM projects employer
characteristics and employer benefits (pensions and health insurance) at each simulated job
change.
DYNASIM projects private DB pensions by using DB plan formulas from the Pension
Benefit Guaranty Corporation’s pension insurance modeling system (PIMS). These DB plan
formulas are randomly assigned to DB participants based on broad industry, union status, and
firm size categories, as well as an indicator of whether the firm offers both DB and DC plans.
For government pensions, DYNASIM uses actual benefit formulas to calculate benefits for
federal government workers and military personnel; to estimate pension benefits for state and
local government workers, DYNASIM assigns workers to one of 481 state and local government
pensions from the Urban Institute’s State and Local Employee Pension Plan (SLEPP) database
based on the workers’ state and job sector (Urban Institute, 2016).
Projected DB pension information reflects pension plan structures through December
2008, including DB pension plan freezes and conversions to cash balance plans.3 DYNASIM
assumes all nonunion private-sector DB pensions will experience a hard freeze between 2008
and 2016 and also assumes two-thirds of state and local pensions will experience a soft freeze
between 2008 and 2016.4 (Users can run alternative scenarios by changing these assumptions.)
DYNASIM adjusts worker DB pensions and survivor pensions after initial pension
receipt for cost-of-living adjustments. DYNASIM varies the probability of selecting a joint and
survivor annuity by gender, education, family health status, wealth, and expected pension
3 DYNASIM projects conversions of pension plan type (from DB to cash balance or DB to DC) by using actual plan change information for plans included in the PIMS data through 2008. 4 In a hard freeze, all workers cease accruing DB benefits and the firm switches to a DC plan. In a soft freeze, new workers are offered a DC plan instead of DB plan, and existing workers remain in the DB plan and continue to accrue benefits.
53
income. It also varies DB cost-of-living adjustments by employment sector (i.e., private, federal
government, and state and local government).
Most DB plan formulas assign pension income as a function of workers’ earnings and job
tenure. DB pension benefits are capped by the statutory limitations under Section 415(b)(1)(A).
Most private-sector workers must complete five years of service before they vest in the DB plan.
Changes in job tenure directly affect expected DB pension income.
Social Security
DYNASIM includes a detailed Social Security benefit calculator that accounts for
virtually all benefit provisions under current law (scheduled and payable). The benefit calculator
can also modify many of the current law provisions to simulate Social Security reforms,
including changes in the benefit formula, payroll taxes and the wage cap, changes in the early
and full retirement ages, cost of living adjustments, spouse and survivor provisions, minimum
benefits, and changes to the windfall elimination provisions (WEP) and Government pension
offsets (GPO).
Means-Tested and Nonmeans-Tested Benefits
DYNASIM projects means-tested and nonmeans-tested benefits using models estimated
for MINT6 and MINT7 (Smith et al, 2010). Means-tested benefits include payments from the
Temporary Assistance for Needy Families program. Nonmeans-tested benefits include veterans’
benefits, unemployment insurance, and workers’ compensation payments. DYNASIM starts
with self-reported receipt and benefit amounts from the SIPP. It then models annual transitions
separately for prior year beneficiaries and prior-year nonbeneficiaries. Means-tested and
nonmeans-tested benefits depend on lagged benefit status, lagged SSI receipt, age, marital status,
education, health status, number of children, financial assets, longitudinal earnings, and a
measure of a change in earnings or impending death. DYNASIM first project benefit receipt and
then benefit amounts among beneficiaries.
Total Retirement Income
DYNASIM projects income and assets annually from 2006 to 2087 by age, gender,
marital status, race and ethnicity, poverty status, geographic region, educational attainment, per
54
capita lifetime work years, nativity, and per capita income quintile. Results can be summarized
for any individual year and in numerous ways. Typically income and assets are measured on a
per capita basis in current price-adjusted dollars. Per capita values are half the sum of husband’s
and wife’s values for married individuals and own values for single individuals. Our measure of
per capita lifetime work years is the sum of years with positive earnings since 1951.5 In years
when both the husband and wife have positive earnings, both partners get one year of work
credit. If only one partner works, both partners get half a year of work credit. If neither partner
works, both partners get zero work credits. Single individuals get work credits based on their
own earnings in years they are single. DYNASIM also calculates the income of nonspouse
family members, which is used only for calculating poverty status.
DYNASIM can generate three per capita income measures (census, annuity, and return
income) that vary by the asset income source and by the inclusion of imputed rental income. Per
capita income is the sum of the husband’s and wife’s income divided by two for married
individuals and own income for single individuals. DYNASIM can also calculate equivalent
income that uses the family size parameters used to determine supplemental poverty to adjust for
family size (Short and Garner, 2012). The model also can use these optional measures in
replacement rate calculations based on the highest 35 years of earnings between ages 20 and 70
or the average of earnings received between ages 50 and 54. Poverty measures used to assess
well-being in retirement also include the income of nonspouse family members.
Census income is the sum of per capita earnings, Social Security, SSI, DB pension,
interest, dividends, net rental income, means-tested and nonmeans-tested benefits, and retirement
account withdrawals. Except for the per capita conversion, this is the standard measure the U.S.
Census Bureau uses to calculate personal income. The census income poverty rate includes
family census income (including nonspouse family members) divided by the family poverty
threshold. Equivalent income uses census income divided by the family equivalence factor
(relative to a two-adult, two-child family).
Annuity income is the sum of per capita earnings, Social Security, SSI, DB pension, and
annuitized asset income. The annuity income poverty rate uses family annuity income (including
nonspouse family members) divided by the family poverty threshold.
5 Early cohorts have censored work years because DYNASIM4’s earnings data begin in 1951. We do not measure work years before 1951.
55
The annuitized asset income measure calculates income from retirement accounts and
financial assets each year as the real (price-indexed), actuarially fair annuity income a family
would receive if it annuitized 80 percent of its retirement accounts and other financial assets
(using a 3 percent annual real return). The annuity factor is recalculated each year to reflect
changes in wealth as individuals age, based on DYNASIM projections of wealth accumulation
and spenddown and changes in life expectancy and marital status as individuals survive to older
ages. For married couples, DYNASIM assumes a 50 percent survivor annuity.
The annuity measure ensures comparability with DB pension and Social Security
benefits, which are also annuities. Without this type of adjustment, DYNASIM would overstate
the loss in retirement well-being from the shift from DB pension income to DC assets. A dollar
in DB pension wealth produces more income by standard measures than a dollar in DC wealth
because measured DB income counts both a return on accumulated assets and some return of
principal, but measured income from financial wealth includes only the return on accumulated
assets and realized retirement account withdrawals. The annuity measure differs conceptually
from the Census measure, which includes only the rate of return on assets (interest, dividends,
and rental income) and excludes the potential consumption of capital that could be realized if a
person spent down his or her wealth.6
Return income is the sum of per capita earnings, Social Security, SSI, DB pension, and a
6 percent return on retirement and financial assets. The return income poverty rate includes
family return income (including nonspouse family members) divided by the family poverty
threshold.
The return income measure provides a measure of well-being from both income-
generating assets (like stocks, bonds, and savings accounts) and non-income-generating assets
(like vehicles and vacation property), but it does not include a factor based on life expectancy. A
problem with the annuity measure is that it typically shows asset income increasing with age
because the remaining assets support fewer years of remaining life. In reality, the assets of
seniors generally decline with age. The return measure captures this pattern. Unlike the Census
measure, which excludes asset values inside of retirement accounts unless they are withdrawn,
the return income includes notional returns on retirement accounts.
6 We calculate annuitized retirement accounts and financial (nonpension) assets by using the same annuity factors.
56
The model optionally captures the amount by which homeowners are better off than those
without any home equity. DYNASIM imputes a 3 percent rate of return to housing equity
(imputed rent) that represents the savings in rent from owning a home, net of costs of interest and
home maintenance. DYNASIM does not project the rapid increase in home values between
2004 and 2006, nor does it project the rapid decline in home values between 2006 and 2009.7
Instead, it projects steadier, smoothed growth in home equity through this period based on PSID
and HRS estimated models of home equity. It accounts for the housing bubble and bust by using
an out-of-the-model adjustment calculated from the 2006-2014 American Community Survey
data at the state-level.
7 Standard & Poor’s. 2011. S&P/Case-Schiller Home Price Indices. http://us.spindices.com/index-family/real-estate/sp-case-shiller.
57
Appendix Table 1. Men’s Labor Force Participation Rates, by Age and Birth Cohort (%)
Age 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 65-70 1931-35 97.7 96.7 94.3 92.0 88.1 77.0 47.2 28.0 1936-40 97.3 97.2 95.5 94.2 92.1 87.4 75.4 50.4 29.1 1941-45 87.9 95.8 96.2 95.2 93.2 91.9 84.4 74.4 53.6 36.0 1946-50 84.7 94.6 96.5 94.4 93.6 90.5 85.3 75.0 54.5 33.9 1951-55 86.4 94.0 94.4 94.5 90.4 89.5 84.1 76.5 57.9 1956-60 87.3 93.6 93.4 91.6 91.7 89.0 83.5 75.6 1961-65 87.6 93.0 93.3 92.9 90.5 87.4 83.4 1966-70 85.6 92.8 93.5 91.7 89.3 87.4 1971-75 85.0 91.8 92.6 91.3 89.8 1976-80 84.5 90.3 90.7 89.3 1981-85 82.4 88.2 90.1 1986-90 77.4 88.3
Note: The labor force participation rate is the share of the civilian noninstitutionalized population that is working or looking for work. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
Appendix Table 2. Women’s Labor Force Participation Rates, by Age and Birth Cohort (%)
Age 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 65-70 1931-35 40.2 51.5 58.1 64.5 60.8 52.8 34.5 18.0 1936-40 37.4 46.9 58.4 67.0 68.1 64.9 55.9 37.3 20.8 1941-45 48.2 44.7 54.4 66.4 72.3 73.8 70.3 59.6 42.0 25.2 1946-50 55.5 57.1 66.9 72.8 78.1 77.3 72.4 63.5 47.0 25.7 1951-55 64.1 66.9 71.4 75.3 78.5 79.0 73.3 65.3 47.1 1956-60 69.3 70.9 73.6 76.9 79.1 77.2 72.3 65.6 1961-65 72.0 73.2 74.1 77.7 77.3 76.0 72.4 1966-70 70.8 75.1 75.7 74.7 76.7 75.3 1971-75 72.7 76.1 73.2 75.1 74.9 1976-80 74.8 73.7 73.2 74.9 1981-85 71.4 73.5 73.0 1986-90 70.2 75.1
Note: The labor force participation rate is the share of the civilian noninstitutionalized population that is working or looking for work. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
58
Appendix Table 3. Men’s Full-Time Employment Rates (%)
Age 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 1931-35 95.8 95.9 92.2 89.5 85.8 74.7 43.7 20.3 1936-40 94.4 95.7 93.1 92.1 88.2 83.4 71.0 46.3 21.7 1941-45 85.4 93.7 93.5 92.7 90.3 88.4 81.9 71.6 48.4 25.7 1946-50 79.8 90.5 93.1 90.2 90.0 87.8 82.6 71.2 48.2 26.4 1951-55 78.7 89.0 89.8 90.3 86.5 87.2 79.6 69.2 52.4 1956-60 81.0 87.3 88.9 87.9 89.3 85.4 75.7 70.5 1961-65 76.7 87.5 87.7 90.4 86.4 79.0 79.0 1966-70 76.1 85.2 90.3 87.7 80.7 82.3 1971-75 72.7 87.0 87.8 82.2 84.2 1976-80 75.0 84.0 80.8 83.9 1981-85 69.2 75.3 82.8 1986-90 56.0 78.9
Note: The table shows the percentage of civilian noninstitutionalized men employed full time. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Appendix Table 4. Women’s Full-Time Employment Rates (%)
Age 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 1931-35 34.6 40.7 42.1 49.9 47.1 40.7 25.4 8.6 1936-40 36.1 37.1 43.4 51.0 54.0 52.5 45.3 27.8 12.3 1941-45 51.0 40.1 40.4 49.0 56.7 60.1 57.0 48.7 32.1 15.0 1946-50 56.3 49.6 50.4 55.0 61.9 63.2 61.4 51.5 35.4 15.8 1951-55 57.2 55.9 54.0 59.0 62.5 65.9 60.4 51.4 36.7 1956-60 60.7 57.8 58.4 59.2 63.9 63.8 57.4 54.1 1961-65 59.0 61.7 58.4 61.7 61.1 59.1 59.4 1966-70 59.4 61.8 62.5 59.7 59.0 60.6 1971-75 56.7 66.5 59.2 57.4 60.5 1976-80 58.8 62.1 56.8 60.2 1981-85 53.4 57.6 59.7 1986-90 44.7 61.2
Note: The table shows the percentage of civilian noninstitutionalized women employed full time. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
59
Appendix Table 5. Men’s Median Earnings in Inflation-Adjusted 2015 Dollars, Full-Time Workers, by Age and Birth Cohort
Age 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 1931-35 50,300 57,100 58,500 58,300 56,000 53,800 48,900 41,900 1936-40 45,800 55,900 58,500 58,300 59,100 57,500 52,200 48,900 43,700 1941-45 34,300 50,200 56,700 58,300 60,500 59,400 55,300 55,900 55,500 48,100 1946-50 37,300 47,400 52,500 57,700 58,300 58,500 62,900 59,200 55,300 54,700 1951-55 33,700 43,700 49,300 55,700 55,300 58,700 59,200 55,300 54,400 1956-60 32,100 41,400 47,300 50,600 55,900 56,800 55,300 55,500 1961-65 28,000 39,000 44,300 53,100 54,300 55,300 55,500 1966-70 27,800 37,100 48,900 51,100 53,100 55,500 1971-75 25,300 41,900 46,900 52,000 55,500 1976-80 28,000 37,000 44,200 50,500 1981-85 27,500 38,600 47,500 1986-90 26,500 38,900
Note: Estimates are rounded to the nearest $100. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Appendix Table 6. Women’s Median Earnings in Inflation-Adjusted 2015 Dollars, Full-Time Workers, by Age and Birth Cohort
Age 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 1931-35 22,900 26,200 29,200 29,100 32,100 32,300 29,200 30,800 1936-40 21,400 25,700 27,400 29,100 32,300 33,400 31,600 33,600 29,600 1941-45 22,000 26,100 28,700 29,100 33,400 37,100 34,900 36,300 35,800 38,700 1946-50 23,700 29,200 29,600 33,600 37,100 36,400 40,500 39,500 39,800 40,400 1951-55 23,500 29,100 33,600 35,300 37,900 40,500 43,200 42,000 40,400 1956-60 23,300 31,400 33,400 34,800 38,200 39,500 42,000 40,400 1961-65 23,400 30,600 33,200 37,700 39,500 39,800 40,400 1966-70 23,300 31,600 36,300 38,900 39,800 41,100 1971-75 22,900 35,000 37,000 40,900 40,400 1976-80 25,200 34,000 38,700 41,200 1981-85 24,700 34,300 38,600 1986-90 24,300 35,300
Note: Estimates are rounded to the nearest $100. Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
60
Appendix Table 7. Percentage of Full-Time Male Workers Participating in an Employer-Sponsored Retirement Plan, by Age and Birth Cohort
Age 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 1931-35 63.8 62.1 56.6 52.7 37.8 1936-40 60.2 62.1 58.8 57.5 50.6 35.6 1941-45 61.0 59.4 58.5 59.2 60.1 52.9 39.7 1946-50 57.8 59.8 58.3 60.5 61.8 56.9 53.0 34.3 1951-55 51.8 52.6 53.9 57.1 61.5 57.6 54.2 42.2 1956-60 40.9 44.2 48.8 55.1 58.5 54.7 53.5 46.1 1961-65 30.7 40.6 49.2 54.9 51.7 52.6 43.4 1966-70 27.6 39.9 50.6 49.3 50.4 41.8 1971-75 28.6 43.5 45.5 48.5 40.1 1976-80 31.5 35.9 43.3 38.6 1981-85 26.4 37.1 35.5 1986-90 28.7 31.1
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
Appendix Table 8. Percentage of Full-Time Female Workers Participating in an Employer-Sponsored Retirement Plan, by Age and Birth Cohort
Age 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 1931-35 54.3 57.3 54.5 51.4 37.7 1936-40 54.6 54.7 54.1 55.2 55.7 38.5 1941-45 51.6 54.6 55.0 59.7 59.7 53.4 44.7 1946-50 52.9 53.5 55.2 59.9 63.4 61.5 55.7 42.7 1951-55 50.9 51.9 53.9 57.1 60.2 59.8 57.5 46.6 1956-60 39.1 45.3 49.4 53.8 57.8 57.4 56.1 50.0 1961-65 33.3 42.3 50.8 56.0 55.9 54.3 47.3 1966-70 29.2 46.2 51.7 50.8 52.5 46.2 1971-75 28.5 48.0 47.8 52.4 42.0 1976-80 34.4 42.4 48.5 40.5 1981-85 29.2 43.5 37.6 1986-90 30.9 32.5
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
61
Appendix Table 9. Percentage of Men Married, by Age and Birth Cohort
Age 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 65-70 1931-35 86.7 87.6 84.3 83.1 82.5 79.6 80.6 80.6 1936-40 80.0 84.3 84.6 80.2 80.7 79.7 80.8 77.4 76.4 1941-45 55.4 76.2 80.7 78.9 77.9 76.4 77.0 75.5 77.7 74.5 1946-50 49.5 71.0 73.9 75.8 75.1 76.4 76.0 75.1 73.8 73.8 1951-55 42.1 61.5 67.7 69.5 69.2 71.0 70.1 70.4 71.7 1956-60 34.1 54.1 62.1 66.2 70.1 68.2 69.5 68.0 1961-65 27.4 50.3 60.3 66.6 66.4 66.5 65.9 1966-70 22.2 46.1 61.6 65.4 65.3 64.8 1971-75 20.4 45.5 60.6 65.1 66.8 1976-80 18.8 42.0 55.8 64.1 1981-85 15.9 36.2 51.9 1986-90 12.4 32.7
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
Appendix Table 10. Percentage of Women Married, by Age and Birth Cohort
Age 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 65-70
1931-35 86.6 83.6 81.2 75.8 72.5 69.0 63.4 56.9 1936-40 85.9 83.6 78.9 75.5 72.8 71.1 68.2 63.0 58.0 1941-45 71.5 80.6 79.0 75.9 72.0 69.3 69.6 65.0 61.8 57.2 1946-50 64.8 75.4 72.9 72.2 70.0 69.6 66.7 64.5 61.5 57.7 1951-55 56.2 67.0 70.1 70.1 68.0 67.6 64.9 64.4 61.8 1956-60 47.6 62.3 67.3 68.1 68.4 66.6 64.6 62.4 1961-65 43.1 59.3 65.8 68.6 65.8 64.7 63.9 1966-70 36.4 56.2 65.6 65.9 66.4 65.6 1971-75 31.9 54.6 65.3 63.9 64.9 1976-80 29.9 53.0 61.6 64.7 1981-85 26.6 47.1 58.1 1986-90 20.5 42.2
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey.
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Appendix Table 11. Percentage of Adults Who Own a Home, by Age and Birth Cohort
Age 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 65-70 1931-35 75.0 79.2 79.6 78.6 80.7 80.8 1936-40 71.5 76.7 75.0 76.8 78.4 78.3 78.1 1941-45 65.4 73.4 70.5 73.6 75.8 77.2 79.6 78.8 1946-50 49.1 64.3 66.1 69.3 73.1 75.9 78.0 77.1 76.8 1951-55 20.4 47.6 55.2 62.4 66.6 72.0 73.0 75.0 73.8 1956-60 21.4 38.0 51.5 60.5 68.9 70.4 70.5 71.8 1961-65 15.0 33.9 51.5 62.7 66.8 66.4 67.3 1966-70 11.9 33.7 53.8 62.0 63.2 61.6 1971-75 11.3 35.0 53.3 55.8 58.5 1976-80 13.0 35.2 45.6 51.4 1981-85 12.9 28.4 40.6 1986-90 8.9 24.3
Source: Authors’ estimates from the Annual Social and Economic supplement to the Current Population Survey. Appendix Table 12. Median per Capita Inflation-Adjusted Household Net Worth by Age and Birth Cohort ($)
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 119,100 121,500 169,100 151,900 146,800 1934-39 123,500 127,100 160,900 167,200 132,500 1940-45 96,500 119,600 197,600 198,500 167,300 1946-51 74,500 81,200 140,300 199,800 162,500 1952-57 45,900 55,400 108,500 167,700 120,300 1958-63 16,500 38,400 79,900 114,000 95,200 1964-69 19,000 39,100 86,900 64,100 1970-75 20,300 43,300 43,000 1976-81 16,400 21,600 1982-87 9,500
Note: Estimates are reported in inflation-adjusted 2015 dollars, rounded to the nearest hundred dollars. The analysis divided total household new worth by two for married adults. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
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Appendix Table 13. Percentage of Adults with Financial Assets, by Age and Birth Cohort
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 90.4 91.5 96.2 99.7 97.6 1934-39 93.1 93.6 93.9 97.0 98.1 1940-45 92.4 93.2 97.0 97.5 97.6 1946-51 92.1 94.3 95.5 97.8 98.3 1952-57 92.1 93.6 95.3 96.6 96.2 1958-63 86.3 93.0 94.4 94.0 94.3 1964-69 90.7 94.4 93.8 95.3 1970-75 93.1 94.1 94.4 1976-81 91.2 93.8 1982-87 94.0
Note: Estimates include retirement accounts. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
Appendix Table 14. Median per Capita Inflation-Adjusted Value of Household Financial Assets for Adults with Positive Holdings, by Age and Birth Cohort ($)
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 34,200 37,900 54,000 32,900 27,300 1934-39 27,000 40,000 60,000 44,500 35,100 1940-45 17,800 29,800 60,200 55,200 50,500 1946-51 16,300 22,400 53,400 65,300 52,000 1952-57 10,400 17,400 36,900 54,400 40,100 1958-63 5,100 11,000 29,700 32,700 31,700 1964-69 8,300 15,300 24,100 22,000 1970-75 7,600 11,600 17,900 1976-81 7,200 10,800 1982-87 4,900
Note: Estimates are reported in inflation-adjusted 2015 dollars, rounded to the nearest hundred dollars, and include the value of retirement accounts. The analysis divided the value of household financial assets by two for married adults. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
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Appendix Table 15. Percentage of Adults with a Retirement Account, by Age and Birth Cohort
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 54.9 45.0 43.9 38.2 30.8 1934-39 54.3 57.1 51.7 51.7 34.2 1940-45 51.5 58.6 67.4 54.8 48.4 1946-51 56.4 57.4 67.8 68.0 56.5 1952-57 47.1 60.0 65.3 70.4 62.9 1958-63 34.8 56.7 66.8 65.5 61.3 1964-69 51.0 61.1 63.1 61.4 1970-75 54.2 57.3 60.2 1976-81 49.0 54.7 1982-87 47.3
Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
Appendix Table 16. Median per Capita Inflation-Adjusted Value of Household Retirement Accounts for Accountholders, by Age and Birth Cohort ($)
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 24,900 33,600 45,900 33,500 40,900 1934-39 26,800 27,400 72,900 43,900 81,800 1940-45 14,900 25,000 37,800 78,000 86,400 1946-51 11,500 25,000 47,300 76,300 86,900 1952-57 7,600 15,600 33,800 56,100 65,400 1958-63 3,800 9,800 24,000 43,300 61,300 1964-69 5,500 14,900 29,000 42,800 1970-75 6,800 13,900 30,000 1976-81 8,500 18,500 1982-87 8,000
Note: Estimates are reported in inflation-adjusted 2015 dollars, rounded to the nearest hundred dollars. The analysis divided the value of household retirement accounts by two for married adults. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
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Appendix Table 17. Percentage of Adults with Housing Wealth, by Age and Birth Cohort
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 83.3 82.9 87.0 86.1 84.6 1934-39 84.4 86.0 85.5 87.7 85.7 1940-45 77.1 84.6 85.5 88.0 86.0 1946-51 73.4 76.2 83.0 85.4 85.9 1952-57 67.6 72.2 79.4 83.2 79.2 1958-63 48.1 59.6 74.0 78.6 75.4 1964-69 49.5 65.9 73.1 70.9 1970-75 52.0 67.3 67.5 1976-81 51.5 60.8 1982-87 44.8
Note: Estimates refer to gross housing wealth. Net housing wealth, or home equity, was negative for people whose housing debt exceeded their housing wealth. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
Appendix Table 18. Median per Capita Inflation-Adjusted Housing Wealth for Adults with Housing Wealth, by Age and Birth Cohort ($)
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 73,600 92,000 132,900 168,600 153,300 1934-39 86,900 91,000 132,900 173,700 184,000 1940-45 81,800 102,200 153,300 235,100 184,000 1946-51 102,200 115,500 145,100 251,400 194,200 1952-57 75,600 100,200 143,100 255,500 194,200 1958-63 66,400 100,200 143,100 235,100 204,400 1964-69 86,900 117,500 245,300 189,100 1970-75 102,200 202,400 204,400 1976-81 204,400 166,600 1982-87 153,300
Note: Estimates are reported in inflation-adjusted 2015 dollars, rounded to the nearest hundred dollars. The analysis divided the value of housing wealth by two for married adults. Estimates refer to gross housing wealth. Net housing wealth, or home equity, was negative for people whose housing debt exceeded their housing wealth. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
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Appendix Table 19. Percentage of Adults with Outstanding Debt, by Age and Birth Cohort
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 75.5 64.3 54.4 47.2 37.3 1934-39 87.4 73.9 68.3 61.7 54.0 1940-45 88.2 84.3 76.0 75.6 63.9 1946-51 90.7 87.4 83.2 82.4 74.3 1952-57 88.7 87.8 88.1 88.6 81.2 1958-63 87.2 89.9 90.5 87.9 82.3 1964-69 86.6 89.1 87.3 87.3 1970-75 86.4 88.8 85.9 1976-81 88.7 85.6 1982-87 80.9
Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
Appendix Table 20. Median per Capita Inflation-Adjusted Value of Outstanding Debt for Debtholders, by Age and Birth Cohort ($)
Age 26-31 32-37 38-43 44-49 50-55 56-61 62-67 68-73 74-79 80-85 1928-33 10,500 9,400 13,200 17,700 25,500 1934-39 19,100 20,500 13,500 19,400 15,900 1940-45 26,800 34,900 26,300 38,700 24,500 1946-51 38,500 39,000 41,200 50,600 34,700 1952-57 33,800 39,900 50,900 63,600 46,500 1958-63 16,300 35,400 54,100 69,900 58,800 1964-69 23,600 49,200 77,500 59,300 1970-75 35,500 72,200 65,900 1976-81 40,500 63,100 1982-87 30,700
Note: Estimates are reported in inflation-adjusted 2015 dollars, rounded to the nearest hundred dollars. The analysis divided the value of household debt by two for married adults. Source: Authors’ estimates from the Survey of Consumer Finances, 1983-2013.
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